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Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 102 - 118 Titre : Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data Type de document : Article/Communication Auteurs : Alfonso Fernández-Manso, Auteur ; Carmen Quintano, Auteur ; Dar A. Roberts, Auteur Année de publication : 2019 Article en page(s) : pp 102 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de mélange spectral d’extrémités multiples [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] entropie [Termes IGN] forêt méditerranéenne [Termes IGN] image EO1-Hyperion [Termes IGN] incendie de forêt Résumé : (Auteur) All ecosystems and in particular ecosystems in Mediterranean climates are affected by fires. Knowledge of the drivers that most influence burn severity patterns as well an accurate map of post-fire effects are key tools for forest managers in order to plan an adequate post-fire response. Remote sensing data are becoming an indispensable instrument to reach both objectives. This work explores the relative influence of pre-fire vegetation structure and topography on burn severity compared to the impact of post-fire damage level, and evaluates the utility of the Maximum Entropy (MaxEnt) classifier trained with post-fire EO-1 Hyperion data and pre-fire LiDAR to model three levels of burn severity at high accuracy. We analyzed a large fire in central-eastern Spain, which occurred on 16–19 June 2016 in a maquis shrubland and Pinus halepensis forested area. Post-fire hyperspectral Hyperion data were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA) and five fraction images were generated: char, green vegetation (GV), non-photosynthetic vegetation, soil (NPVS) and shade. Metrics associated with vegetation structure were calculated from pre-fire LiDAR. Post-fire MESMA char fraction image, pre-fire structural metrics and topographic variables acted as inputs to MaxEnt, which built a model and generated as output a suitability surface for each burn severity level. The percentage of contribution of the different biophysical variables to the MaxEnt model depended on the burn severity level (LiDAR-derived metrics had a greater contribution at the low burn severity level), but MaxEnt identified the char fraction image as the highest contributor to the model for all three burn severity levels. The present study demonstrates the validity of MaxEnt as one-class classifier to model burn severity accurately in Mediterranean countries, when trained with post-fire hyperspectral Hyperion data and pre-fire LiDAR. Numéro de notice : A2019-313 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.003 Date de publication en ligne : 14/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93339 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019091	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019093	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019092	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Object-based image mapping of conifer tree mortality in San Diego county based on multitemporal aerial ortho-imagery / Mary Pyott Freeman in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 7 (juillet 2016) . - pp 571 - 580 Titre : Object-based image mapping of conifer tree mortality in San Diego county based on multitemporal aerial ortho-imagery Type de document : Article/Communication Auteurs : Mary Pyott Freeman, Auteur ; Douglas A. Stow, Auteur ; Dar A. Roberts, Auteur Année de publication : 2016 Article en page(s) : pp 571 - 580 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] arbre mort [Termes IGN] carte de la végétation [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] image aérienne [Termes IGN] image multitemporelle [Termes IGN] orthoimage [Termes IGN] Pinophyta [Termes IGN] San Diego Résumé : (Auteur) Two GEOBIA approaches are compared for their effectiveness in mapping dead trees within island montane forests of Southern California: a spatial contextual approach using an artificial neural network classifier, and a segmentation and multi-pixel classification approach. Both approaches are tested with multitemporal aerial orthoimagery having varying spatial resolutions. Spectral transformation inputs are also tested. An object-based accuracy assessment is conducted. Accuracies range between 30 percent to 90 percent for the dead tree class and are significantly higher for the spatial-contextual approach. Inclusion of spectral transforms increased accuracies by 5 percent for the true object-based approach, up to 13 percent for the spatial contextual approach, and reduced commission error up to 10 percent for both approaches. Masking techniques increased accuracies of the spatial contextual approach by 20 percent. With manual editing, the most accurate maps of individual live and dead trees from the spatial contextual approach are suitable for studying spatio-temporal trends in montane conifer mortality. Numéro de notice : A2016-518 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.7.571 En ligne : http://dx.doi.org/10.14358/PERS.82.7.571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81589 [article]

Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China / Liguo Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 41 - 47 Titre : Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China Type de document : Article/Communication Auteurs : Liguo Zhou, Auteur ; Dar A. Roberts, Auteur ; Weichun Ma, Auteur ; Hao Zhang, Auteur ; Lin Tang, Auteur Année de publication : 2014 Article en page(s) : pp 41 - 47 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Chine [Termes IGN] chlorophylle [Termes IGN] image HJ-1A [Termes IGN] image hyperspectrale [Termes IGN] lac Résumé : (Auteur) Based on in situ water sampling and field spectral measurements in Dianshan Lake, a semi-analytical three-band algorithm was used to estimate Chlorophylla (Chla) content in case II waters. The three bands selected to estimate Chla for high concentrations included 653, 691 and 748 nm. An equation, based on the difference in reciprocal reflectance between 653 and 691 nm, multiplied by reflectance at 748 nm as [Rrs-1(653) - Rrs-1(691)] Rrs(748), explained 85.57% of variance in Chla concentration with a root mean square error (RMSE) of Numéro de notice : A2014-084 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32989 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014021	RAB	Revue	Centre de documentation	En réserve L003	Disponible